Summary of Spatiotemporal Object Detection For Improved Aerial Vehicle Detection in Traffic Monitoring, by Kristina Telegraph and Christos Kyrkou
Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring
by Kristina Telegraph, Christos Kyrkou
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents advancements in multi-class vehicle detection using unmanned aerial vehicle (UAV) cameras through the development of spatiotemporal object detection models. The study introduces a new dataset, the Spatio-Temporal Vehicle Detection Dataset (STVD), containing 6,600 annotated sequential frame images captured by UAVs. This dataset enables comprehensive training and evaluation of algorithms for holistic spatiotemporal perception. A YOLO-based object detection algorithm is enhanced to incorporate temporal dynamics, resulting in improved performance over single-frame models. The integration of attention mechanisms into spatiotemporal models is shown to further enhance performance. Experimental validation demonstrates significant progress, with the best spatiotemporal model exhibiting a 16.22% improvement over single-frame models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper improves the ability to detect vehicles using cameras on drones. The team created a new dataset of images taken by drones and used it to train computers to recognize vehicles in both space and time. They also developed a way to make their algorithm better at recognizing vehicles by incorporating information from previous frames. This approach led to a 16% improvement over previous methods. The paper shows that this technology has the potential to be even better if they add more features. |
Keywords
» Artificial intelligence » Attention » Object detection » Spatiotemporal » Yolo
